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Evolutionary Multiprocessor Task Scheduling

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4 Author(s)
Montazeri, F. ; Silicon Intelligence & VLSI Signal Process. Lab., Tehran Univ. ; Salmani-Jelodar, M. ; Fakhraie, S.N. ; Fakhraie, S.M.

The genetic algorithm has, to date, been applied to a wide range of problems. It is an ideal tool to solve problem in need of multiple, often interdependent requirements. This is because it has the ability to search within a large solution space while at the same time meeting criteria and constraints within the problem's boundaries. In this paper, we apply this heuristic to the problem of multiprocessor task scheduling - assigning a group of predefined tasks to a set of predefined processors. This task execution should take a minimum amount of time while taking into account certain constraints - e.g., prerequisite constraints between the tasks. Aside from using the genetic algorithm, we incorporate a local search method called a memetic within the genetic algorithm as a global search. Since the tasks are operating in a multiprocessor environment, we also attempt to reduce processor temperature by reducing the total power consumption and load balancing amongst the processors

Published in:

Parallel Computing in Electrical Engineering, 2006. PAR ELEC 2006. International Symposium on

Date of Conference:

13-17 Sept. 2006